Learning from Censored and Truncated Data in Practice
An experimental study of the methods and algorithms developed to learn from truncated data. In my work, I provide a theoretical framework used to learn from missing data, and then show results from the package that I have developed to alleviate such biases.
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| Formato: | Tesis |
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Massachusetts Institute of Technology
2022
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| Acceso en línea: | https://hdl.handle.net/1721.1/144548 |